Explore the vast range of titles available.

This study presents the development of an ontology-based customisation management system (Onto-CMS) for driver–vehicle interfaces (DVIs) in highly automated vehicles (HAVs). The objective of the proposed system is to enhance safety, minimise the probability of accidents and address legal liability concerns. The study highlights the importance of DVIs in automated vehicles and the need for safe and adaptable options for human drivers, while also considering the legal implications associated with the development of these interfaces. The research identifies the shortcomings of existing systems and proposes the Onto-CMS as a more effective alternative solution. The proposed system facilitates additional personalisation tasks and demonstrates higher performance compared to systems lacking ontological structuring. Indeed, the Onto-CMS allows dynamic adaptation to individual preferences while maintaining the integrity of standardised safety elements. It is distinguished by its ability to adjust to diverse contexts, such as those involving impaired drivers, without compromising critical safety standards. The onto-CMS reduces the need for recurrent revisions and improves operational productivity and overall usability. The results show that the Onto-CMS improves the configuration of DVIs by providing customised, scalable and context-aware alternatives. The study provides a basis for further research that could extend the system’s capabilities to cover a wider range of driver characteristics and requirements.

The growing adoption of Artificial Intelligence (AI) in critical sectors raises increasing concerns about regulatory compliance. The European Union Artificial Intelligence Act (EU AI Act) represents the first comprehensive legal framework specifically designed to govern AI systems, classifying them according to their associated risk levels. This paper presents an AI-based compliance checker developed to support the assessment of AI systems under the EU AI Act. The tool is designed to help developers understand legal obligations and identify potential compliance risks through structured, contextualized analysis. We describe the system’s architecture, which combines a generative language model with legal document retrieval techniques to interpret regulatory requirements. The tool was tested on a range of real-world use cases, including algorithmic hiring, medical diagnostics, smart city traffic management, and facial recognition in retail environments. Evaluation was conducted through both automated analysis and expert review. The results confirm the system’s effectiveness in understanding and reconstructing core regulatory obligations, particularly in assessing risk levels and suggesting improvements. While the tool performed strongly in central legal areas, some limitations emerged in handling highly technical or cross-regulatory content. Expert feedback highlighted its utility as a pre-audit tool, emphasizing its depth of legal reasoning, transparency, and contextual adaptability.

This study introduces a hybrid methodological approach for personalised clinical decision support, integrating SHACL-based deterministic constraints with Bayesian probabilistic models. The primary goal is to validate the model and demonstrate the benefits of combining encoded clinical knowledge with probabilistic uncertainties in managing complex therapeutic scenarios. The framework was applied to recurrent urinary tract infections (UTIs) in postmenopausal patients, a clinical context marked by high frequency, treatment challenges, and potential conflicts among therapeutic guidelines. Realistic simulated case studies were developed, encompassing both simple clinical profiles and complex situations, such as patients with antibiotic resistance. Each profile was modelled in RDF/Turtle, enabling semantic representation of clinical features and therapeutic rules. The system automatically calculates success and failure probabilities for different therapeutic scenarios, dynamically adapting them based on follow-up data. This allows clinicians to assess not only the initial therapy choice (Case study no. 1) but also the potential addition of supplementary interventions during treatment (Case study no. 2). Results highlight that the proposed hybrid SHACL–Bayesian framework enables tightly coupled deterministic–probabilistic reasoning, where SHACL constraints define the admissible clinical decisions and Bayesian inference operates within this validated space. Compared to deterministic or probabilistic approaches, the combined framework more effectively handles uncertainty, guideline conflicts, and temporal updates. The scientific contribution lies in showing that this integration enhances decision support for recurrent UTIs in postmenopausal patients, providing clinically consistent, transparent, and adaptive therapeutic recommendations aligned with the patient’s evolving condition.

Organisations heavily dependent on paper documents still spend a significant amount of time managing a large volume of documents. An intelligent document management system (DMS) is presented to automate the processing of tax and administrative documents. The proposed system fills a gap in the landscape of practical tools in the field of DMS and advances the state of the art. This system represents a complex process of integrated AI-powered technologies that creates an ontology, extracts information from documents, defines profiles, maps the extracted data in RDF format, and applies inference through a reasoning engine. The DMS was designed to help all those companies that manage their clients’ tax and administrative documents daily. Automation speeds up the management process so that companies can focus more on value-added services. The system was tested in a case study that focused on the preparation of tax returns. The results demonstrated the efficacy of the system in providing document management service.

This study examines the effectiveness of ICT training programmes for academics at selected universities in Africa, specifically in Cameroon, Nigeria, and South Africa. Using a qualitative approach, we conducted semi-structured interviews with five ICT trainers and three trainees to better understand their experiences. The findings highlight both the challenges and the strengths that could be used to improve ICT training and make it more effective and relevant to the participants. Four key themes emerge from our analysis: course objectives and content, teaching strategies, implementation challenges, and expectations/impact of the courses. The objectives of the courses vary considerably—some focus on the practical application of ICT, while others aim to improve the participants’ understanding of ICT systems. Teaching methods also differ, ranging from more traditional teaching approaches to collaborative, project-oriented methods. However, we identify significant challenges, particularly poor internet connectivity and a lack of sufficient technological resources, which affect the learning process. In addition, there is often a divergence between trainer expectations and trainee perceptions of the impact of these courses on their professional development. This study aims to contribute to the definition of strategies to improve the effectiveness of ICT training, in the African context, including Cameroon, Nigeria and South Africa.

Monitoring University students’ progress, in an interactive, synchronised, and coordinated way across a given study program is a challenge. Indeed, for a given student journey, University actors need a service offering personalised views and actions depending on their role (students, study advisors, scientific committee, faculty members, admission office), all linked to the same underlying information system, and abiding study regulations. The challenge is even greater when it comes to scaling up and providing such service for the whole University community (approx. 23K members in Geneva). Following a service science approach, we derived a method for automatically generating a service, compliant by design to study regulations, and offering personalised views to the various actors. The automatic implementation of the study regulations is based on the definition of generic rules able to define the different elements of the regulations that need to be respected across all curricula and programs. We provide a case study based on the PhD students regulations. We describe a proof-of-concept instantiating the service from the rules, role-centric views of the interface, as well as the underlying architecture relying on a semantic reasoning engine. The work presented in this paper has the potential to alleviate and improve the tasks of all the various actors involved in students monitoring, going beyond PhD students. This approach automatises the implementations of any study program and can be applied to any University.

Writing study regulations for academic study programs and automatically implementing those regulations is a difficult task that involves a variety of actors and requires at each step careful compliance to the constraints defined in the regulations. This paper describes: (1) the innovation process, taking place through a hands-on experiment, that lead the R&D Unit of the University of Geneva to provide a proposal for a digital service targeting the above purpose; (2) the actual design of such a digital service, providing various functionalities: (a) the elaboration of study regulations; (b) the elaboration of the corresponding study plan; (c) the actual implementation of the study plan through the information system. The digital service relies on two main ideas: (1) all study regulations and study plans are built from common atomic elements, that we call building blocks; (2) ensuring compliance to various constraints is achieved through a reasoning engine capturing the constraints defined over an ontology of the study regulations domain. Each year, for a given University, several study regulations, with various constraints and structure are defined or updated. They all need to be carefully crafted and implemented. The work presented in this paper has the potential to alleviate and improve this task for the various actors involved (students, program directors, lawyers, scientific committee members, study advisor, information systems managers, students’ office).

The increasing complexity in hospital waste management requires innovative solutions that integrate sustainability and regulatory compliance. This study proposes an AI-based decision tool to support the circular management of healthcare waste. The approach combines two key elements: (i) the systematic qualitative analysis of international, European, and national regulations, scientific literature, and best practices aimed at identifying strategic actions; (ii) the prioritization of these actions through machine learning, using a Random Forest classifier. We identified 55 actions, grouped into 13 thematic areas, and used them as input variables to assess their impact on regulatory compliance. The variable importance analysis allowed us to classify actions according to their strategic relevance, guiding the structure of the tool and its user interface. Validation, conducted on four simulated case studies, demonstrated the system’s ability to improve compliance monitoring, operational efficiency, and the implementation of circular economy and Zero-Waste strategies. The proposed model represents a scalable and evidence-based solution capable of supporting the ecological transition of healthcare facilities in line with EU directives and the Sustainable Development Goals.

This study assesses the extent to which an AI-driven circular waste management tool, previously developed by the same authors as a decision-support system for the circular management of healthcare waste in compliance with international guidelines, reflects the operational needs and perceived priorities of healthcare professionals and environmental managers. Within a context characterised by high regulatory complexity and increasing pressure toward more sustainable management models, the research adopts a qualitative approach based on the thematic analysis of 11 semi-structured interviews, followed by a systematic mapping of the emergent themes onto the tool’s thematic areas, indicators, and operational actions. The results demonstrate a high degree of alignment between the tool and operational practice, with 93% of the tool’s actions supported by empirical evidence and the emergence of a shared core cluster focused on hard-to-manage waste streams, mandatory training, and day-to-day operational challenges. The alignment between the priorities expressed by interviewees and the importance scores generated by the computational model is high for actions of greater relevance, while it decreases for less frequent actions that are more context-dependent. Circular economy practices are recognised as relevant but remain predominantly positioned at a strategic rather than an operational level. Overall, the study confirms the conceptual robustness of the tool and identifies its main limitations and the conditions required for its integration into hospital workflows.

Ontologies are essential tools for representing, organizing, and sharing knowledge across various domains. This study presents a methodology for ontology construction supported by large language models (LLMs), with an initial application in the automotive sector. Specifically, a user preference ontology for adaptive interfaces in autonomous machines was developed using ChatGPT-4o. Based on this case study, the results were generalized into a reusable methodology. The proposed workflow integrates classical ontology engineering methodologies with the generative and analytical capabilities of LLMs. Each phase follows well-established steps: domain definition, term elicitation, class hierarchy construction, property specification, formalization, population, and validation. A key innovation of this approach is the use of a guiding table that translates domain knowledge into structured prompts, ensuring consistency across iterative interactions with the LLM. Human experts play a continuous role throughout the process, refining definitions, resolving ambiguities, and validating outputs. The ontology was evaluated in terms of logical consistency, structural properties, semantic accuracy, and inferential completeness, confirming its correctness and coherence. Additional validation through SPARQL queries demonstrated its reasoning capabilities. This methodology is generalizable to other domains, if domain experts adapt the guiding table to the specific context. Despite the support provided by LLMs, domain expertise remains essential to guarantee conceptual rigor and practical relevance.